





"""simple docstring"""





import os

import jsonlines
import numpy as np
from tqdm import tqdm


SCREAMING_SNAKE_CASE__   =  2_048
SCREAMING_SNAKE_CASE__   =  4_096
SCREAMING_SNAKE_CASE__   =  42
SCREAMING_SNAKE_CASE__   =  os.environ.pop("PROCESS_TRAIN", "false")
SCREAMING_SNAKE_CASE__   =  {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      def choose_first(SCREAMING_SNAKE_CASE	:		Any				,		SCREAMING_SNAKE_CASE	:		Optional[Any]=False     ):
            assert isinstance(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
            if len(SCREAMING_SNAKE_CASE     ) == 1:
                  lowerCAmelCase    		=			answer[0]
                  return {k: [answer[k]] for k in answer} if is_long_answer else answer
            for a in answer:
                  if is_long_answer:
                        lowerCAmelCase    		=			{k: [a[k]] for k in a}
                  if len(a["""start_token"""]     ) > 0:
                        break
            return a

      lowerCAmelCase    		=			{"""id""": example["""id"""]}
      lowerCAmelCase    		=			example["""annotations"""]
      lowerCAmelCase    		=			annotation["""yes_no_answer"""]
      if 0 in yes_no_answer or 1 in yes_no_answer:
            lowerCAmelCase    		=			["""yes"""] if 1 in yes_no_answer else ["""no"""]
            lowerCAmelCase    		=			lowerCAmelCase    		=			[]
            lowerCAmelCase    		=			lowerCAmelCase    		=			[]
            lowerCAmelCase    		=			["""<cls>"""]
      else:
            lowerCAmelCase    		=			["""short"""]
            lowerCAmelCase    		=			choose_first(annotation["""short_answers"""]     )
            if len(out["""start_token"""]     ) == 0:
                  # answer will be long if short is not available
                  lowerCAmelCase    		=			["""long"""]
                  lowerCAmelCase    		=			choose_first(annotation["""long_answer"""]				,		is_long_answer=SCREAMING_SNAKE_CASE     )
                  lowerCAmelCase    		=			[]
            answer.update(SCREAMING_SNAKE_CASE     )

      # disregard some samples
      if len(answer["""start_token"""]     ) > 1 or answer["start_token"] == answer["end_token"]:
            lowerCAmelCase    		=			True
      else:
            lowerCAmelCase    		=			False

      lowerCAmelCase    		=			["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
      if not all(isinstance(answer[k]				,		SCREAMING_SNAKE_CASE     ) for k in cols     ):
            raise ValueError("""Issue in ID"""				,		example["""id"""]     )

      return answer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		int=False     ):







      '''simple docstring'''




      lowerCAmelCase    		=			_get_single_answer(SCREAMING_SNAKE_CASE     )
      # bytes are of no use
      del answer["start_byte"]
      del answer["end_byte"]

      # handle yes_no answers explicitly
      if answer["category"][0] in ["yes", "no"]:  # category is list with one element
            lowerCAmelCase    		=			example["""document"""]["""tokens"""]
            lowerCAmelCase    		=			[]
            for i in range(len(doc["""token"""]     )     ):
                  if not doc["is_html"][i]:
                        context.append(doc["""token"""][i]     )
            return {
                "context": " ".join(SCREAMING_SNAKE_CASE     ),
                "answer": {
                    "start_token": -1_00,  # ignore index in cross-entropy
                    "end_token": -1_00,  # ignore index in cross-entropy
                    "category": answer["category"],
                    "span": answer["category"],  # extra
                },
            }

      # later, help in removing all no answers
      if answer["start_token"] == [-1]:
            return {
                "context": "None",
                "answer": {
                    "start_token": -1,
                    "end_token": -1,
                    "category": "null",
                    "span": "None",  # extra
                },
            }

      # handling normal samples

      lowerCAmelCase    		=			["""start_token""", """end_token"""]
      answer.update({k: answer[k][0] if len(answer[k]     ) > 0 else answer[k] for k in cols}     )  # e.g. [10] == 10

      lowerCAmelCase    		=			example["""document"""]["""tokens"""]
      lowerCAmelCase    		=			answer["""start_token"""]
      lowerCAmelCase    		=			answer["""end_token"""]

      lowerCAmelCase    		=			[]
      for i in range(len(doc["""token"""]     )     ):
            if not doc["is_html"][i]:
                  context.append(doc["""token"""][i]     )
            else:
                  if answer["start_token"] > i:
                        start_token -= 1
                  if answer["end_token"] > i:
                        end_token -= 1
      lowerCAmelCase    		=			""" """.join(context[start_token:end_token]     )

      # checking above code
      if assertion:
            lowerCAmelCase    		=			doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
            lowerCAmelCase    		=			doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
            lowerCAmelCase    		=			""" """.join([old[i] for i in range(len(SCREAMING_SNAKE_CASE     )     ) if not is_html[i]]     )
            if new != old:
                  print("""ID:"""				,		example["""id"""]     )
                  print("""New:"""				,		SCREAMING_SNAKE_CASE				,		end="""\n"""     )
                  print("""Old:"""				,		SCREAMING_SNAKE_CASE				,		end="""\n\n"""     )

      return {
          "context": " ".join(SCREAMING_SNAKE_CASE     ),
          "answer": {
              "start_token": start_token,
              "end_token": end_token - 1,  # this makes it inclusive
              "category": answer["category"],  # either long or short
              "span": new,  # extra
          },
      }


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[int]				,		SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		List[Any]=20_48				,		SCREAMING_SNAKE_CASE	:		Any=40_96				,		SCREAMING_SNAKE_CASE	:		Tuple=True     ):







      '''simple docstring'''




      lowerCAmelCase    		=			get_context_and_ans(SCREAMING_SNAKE_CASE				,		assertion=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			out["""answer"""]

      # later, removing these samples
      if answer["start_token"] == -1:
            return {
                "example_id": example["id"],
                "input_ids": [[-1]],
                "labels": {
                    "start_token": [-1],
                    "end_token": [-1],
                    "category": ["null"],
                },
            }

      lowerCAmelCase    		=			tokenizer(example["""question"""]["""text"""]				,		out["""context"""]     ).input_ids
      lowerCAmelCase    		=			input_ids.index(tokenizer.sep_token_id     ) + 1

      # return yes/no
      if answer["category"][0] in ["yes", "no"]:  # category is list with one element
            lowerCAmelCase    		=			[]
            lowerCAmelCase    		=			[]
            lowerCAmelCase    		=			input_ids[:q_len]
            lowerCAmelCase    		=			range(SCREAMING_SNAKE_CASE				,		len(SCREAMING_SNAKE_CASE     )				,		max_length - doc_stride     )
            for i in doc_start_indices:
                  lowerCAmelCase    		=			i + max_length - q_len
                  lowerCAmelCase    		=			input_ids[i:end_index]
                  inputs.append(q_indices + slice     )
                  category.append(answer["""category"""][0]     )
                  if slice[-1] == tokenizer.sep_token_id:
                        break

            return {
                "example_id": example["id"],
                "input_ids": inputs,
                "labels": {
                    "start_token": [-1_00] * len(SCREAMING_SNAKE_CASE     ),
                    "end_token": [-1_00] * len(SCREAMING_SNAKE_CASE     ),
                    "category": category,
                },
            }

      lowerCAmelCase    		=			out["""context"""].split()
      lowerCAmelCase    		=			splitted_context[answer["""end_token"""]]
      lowerCAmelCase    		=			len(
          tokenizer(
              """ """.join(splitted_context[: answer["""start_token"""]]     )				,		add_special_tokens=SCREAMING_SNAKE_CASE				,		).input_ids     )
      lowerCAmelCase    		=			len(
          tokenizer(""" """.join(splitted_context[: answer["""end_token"""]]     )				,		add_special_tokens=SCREAMING_SNAKE_CASE     ).input_ids     )

      answer["start_token"] += q_len
      answer["end_token"] += q_len

      # fixing end token
      lowerCAmelCase    		=			len(tokenizer(SCREAMING_SNAKE_CASE				,		add_special_tokens=SCREAMING_SNAKE_CASE     ).input_ids     )
      if num_sub_tokens > 1:
            answer["end_token"] += num_sub_tokens - 1

      lowerCAmelCase    		=			input_ids[answer["""start_token"""] : answer["""end_token"""] + 1]  # right & left are inclusive
      lowerCAmelCase    		=			answer["""start_token"""]
      lowerCAmelCase    		=			answer["""end_token"""]

      if assertion:
            lowerCAmelCase    		=			tokenizer.decode(SCREAMING_SNAKE_CASE     )
            if answer["span"] != new:
                  print("""ISSUE IN TOKENIZATION"""     )
                  print("""OLD:"""				,		answer["""span"""]     )
                  print("""NEW:"""				,		SCREAMING_SNAKE_CASE				,		end="""\n\n"""     )

      if len(SCREAMING_SNAKE_CASE     ) <= max_length:
            return {
                "example_id": example["id"],
                "input_ids": [input_ids],
                "labels": {
                    "start_token": [answer["start_token"]],
                    "end_token": [answer["end_token"]],
                    "category": answer["category"],
                },
            }

      lowerCAmelCase    		=			input_ids[:q_len]
      lowerCAmelCase    		=			range(SCREAMING_SNAKE_CASE				,		len(SCREAMING_SNAKE_CASE     )				,		max_length - doc_stride     )

      lowerCAmelCase    		=			[]
      lowerCAmelCase    		=			[]
      lowerCAmelCase    		=			[]
      lowerCAmelCase    		=			[]  # null, yes, no, long, short
      for i in doc_start_indices:
            lowerCAmelCase    		=			i + max_length - q_len
            lowerCAmelCase    		=			input_ids[i:end_index]
            inputs.append(q_indices + slice     )
            assert len(inputs[-1]     ) <= max_length, "Issue in truncating length"

            if start_token >= i and end_token <= end_index - 1:
                  lowerCAmelCase    		=			start_token - i + q_len
                  lowerCAmelCase    		=			end_token - i + q_len
                  answers_category.append(answer["""category"""][0]     )  # ["short"] -> "short"
            else:
                  lowerCAmelCase    		=			-1_00
                  lowerCAmelCase    		=			-1_00
                  answers_category.append("""null"""     )
            lowerCAmelCase    		=			inputs[-1][start_token : end_token + 1]

            answers_start_token.append(SCREAMING_SNAKE_CASE     )
            answers_end_token.append(SCREAMING_SNAKE_CASE     )
            if assertion:
                  if new != old and new != [tokenizer.cls_token_id]:
                        print("""ISSUE in strided for ID:"""				,		example["""id"""]     )
                        print("""New:"""				,		tokenizer.decode(SCREAMING_SNAKE_CASE     )     )
                        print("""Old:"""				,		tokenizer.decode(SCREAMING_SNAKE_CASE     )				,		end="""\n\n"""     )
            if slice[-1] == tokenizer.sep_token_id:
                  break

      return {
          "example_id": example["id"],
          "input_ids": inputs,
          "labels": {
              "start_token": answers_start_token,
              "end_token": answers_end_token,
              "category": answers_category,
          },
      }


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Tuple				,		SCREAMING_SNAKE_CASE	:		Optional[int]				,		SCREAMING_SNAKE_CASE	:		List[Any]=20_48				,		SCREAMING_SNAKE_CASE	:		List[str]=40_96				,		SCREAMING_SNAKE_CASE	:		List[str]=False     ):







      '''simple docstring'''




      lowerCAmelCase    		=			get_strided_contexts_and_ans(
          SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		doc_stride=SCREAMING_SNAKE_CASE				,		max_length=SCREAMING_SNAKE_CASE				,		assertion=SCREAMING_SNAKE_CASE				,		)

      return example


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int				,		SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      with jsonlines.open(SCREAMING_SNAKE_CASE				,		"""a"""     ) as writer:
            for example in tqdm(SCREAMING_SNAKE_CASE				,		total=len(SCREAMING_SNAKE_CASE     )				,		desc="""Saving samples ... """     ):
                  lowerCAmelCase    		=			example["""labels"""]
                  for ids, start, end, cat in zip(
                      example["""input_ids"""]				,		labels["""start_token"""]				,		labels["""end_token"""]				,		labels["""category"""]				,		):
                        if start == -1 and end == -1:
                              continue  # leave waste samples with no answer
                        if cat == "null" and np.random.rand() < 0.6:
                              continue  # removing 50 % samples
                        writer.write(
                            {
                                """input_ids""": ids,
                                """start_token""": start,
                                """end_token""": end,
                                """category""": CATEGORY_MAPPING[cat],
                            }     )


if __name__ == "__main__":
       from datasets import load_dataset

       from transformers import BigBirdTokenizer

       SCREAMING_SNAKE_CASE__   =  load_dataset("natural_questions")
       SCREAMING_SNAKE_CASE__   =  BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")

       SCREAMING_SNAKE_CASE__   =  data["train" if PROCESS_TRAIN == "true" else "validation"]

       SCREAMING_SNAKE_CASE__   =  {
           "tokenizer": tokenizer,
           "doc_stride": DOC_STRIDE,
           "max_length": MAX_LENGTH,
           "assertion": False,
       }
       SCREAMING_SNAKE_CASE__   =  data.map(prepare_inputs, fn_kwargs=fn_kwargs)
       SCREAMING_SNAKE_CASE__   =  data.remove_columns(["annotations", "document", "id", "question"])
       print(data)

       np.random.seed(SEED)
       SCREAMING_SNAKE_CASE__   =  "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
       save_to_disk(data, file_name=cache_file_name)













"""simple docstring"""





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


SCREAMING_SNAKE_CASE__   =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       SCREAMING_SNAKE_CASE__   =  json.load(f)

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            return FSMTTokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			FSMTForConditionalGeneration.from_pretrained(lowercase					).to(lowercase					)
            if torch_device == "cuda":
                  model.half()
            return model





      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]					)
      @slow
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Dict:
            # note: this test is not testing the best performance since it only evals a small batch
            # but it should be enough to detect a regression in the output quality
            lowerCAmelCase    		=			f'facebook/wmt19-{pair}'
            lowerCAmelCase    		=			self.get_tokenizer(lowercase					)
            lowerCAmelCase    		=			self.get_model(lowercase					)

            lowerCAmelCase    		=			bleu_data[pair]["""src"""]
            lowerCAmelCase    		=			bleu_data[pair]["""tgt"""]

            lowerCAmelCase    		=			tokenizer(lowercase ,		return_tensors="""pt""" ,		truncation=lowercase ,		padding="""longest"""					).to(lowercase					)
            lowerCAmelCase    		=			model.generate(
                input_ids=batch.input_ids ,		num_beams=8 ,		)
            lowerCAmelCase    		=			tokenizer.batch_decode(
                lowercase ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase					)
            lowerCAmelCase    		=			calculate_bleu(lowercase ,		lowercase					)
            print(lowercase					)
            self.assertGreaterEqual(scores["""bleu"""] ,		lowercase					)






